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Featured in Development

Peter Alvaro talks about the reasons one should engage in language design and why many of us would (or should) do something so perverse as to design a language that no one will ever use. He shares some of the extreme and sometimes obnoxious opinions that guided his design process.

Featured in AI, ML & Data Engineering

Today on The InfoQ Podcast, Wes talks with Katharine Jarmul about privacy and fairness in machine learning algorithms. Jarul discusses what’s meant by Ethical Machine Learning and some things to consider when working towards achieving fairness. Jarmul is the co-founder at KIProtect a machine learning security and privacy firm based in Germany and is one of the three keynote speakers at QCon.ai.

Featured in Culture & Methods

Organizations struggle to scale their agility. While every organization is different, common patterns explain the major challenges that most organizations face: organizational design, trying to copy others, “one-size-fits-all” scaling, scaling in siloes, and neglecting engineering practices. This article explains why, what to do about it, and how the three leading scaling frameworks compare.

People Are More Complex Than Computers: Growing the Equal Experts' Team and Culture

At QCon London 2019, Mairead O'Connor from Equal Experts presented "People are More Complex Than Computers". O'Connor shared her experiences of the growth of the organisation into a network of 1,500 people, with over 800 of them being consultants, and described several of the organisational and cultural challenges that came with creating this unique organisational structure.

As is well-known from computer network communications theory, the number of connections between nodes grows exponentially with the number of nodes. Similarly, in growing companies, communication paths between colleagues grow exponentially, which makes communication and information dissemination rapidly more challenging as the organisation grows. In fact, the number of possible communication paths between colleagues are n*(n-1)/2, where n is the number of people.

Early stage companies, with few members, are undefined and simple in respect to communication. As a company grows, it becomes impossible for everyone to know everyone else, or even know each person's areas of expertise, interests and overlapping knowledge. The challenge then really becomes, "How do you grow as a company without losing what made you special to succeed in the first place?" For Equal Experts, the answer to that was breaking down the problem into smaller pieces, using self-organising teams and the advice process approach.

The advice process approach consists of three phases. First, there is the "state intention" phase. Then comes the "collect feedback" phase, and finally the "make decision" phase. The process still requires executive leadership, escalation routes, and people not assuming that something is someone else's problem. The way that Equal Experts attempts to solve this problem is by encouraging an active effort to spread information around the network. Together with this comes the concept of open communications wherever it is logically and legally possible.

In short, the Equal Experts model of managing organisational growth is being handled in the same way as if they were dealing with scaling a development team. It's trying to avoid organisational monoliths by dividing into geographically dispersed, semi-autonomous business units that share best practices and services amongst them. In team composition, the organisation is leaning towards cross-functional delivery teams, with operations being first class citizens. Embracing the "bright new world of devops", developers are expected to be doing operations tasks as well.

While all the points above were quite straightforward to define, there were some decisions that were particularly difficult for Equal Experts while the organisation was growing. Defining what Human Resources looks like, and how this functions in a decentralised organisation with more than half of its staff being independent consultant,s is the first issue. Another realisation that came pretty quickly is that software metaphors can only take you so far. People have feelings, whereas computers don't.

In real life, you are always testing in production, there is no "staging environment". In software when you make a mistake you can try again many times, or write an automated test to make sure that the same issue won't happen again. In real life, this is impossible. Balancing freedom versus accountability is hard, as is diversity and inclusion when growing a global, distributed organisation. In short, growth of an organisation is neither linear, nor predictable.

What Equal Experts has learned from this process is that bigger is different, and many times you need to dynamically adapt, or "make it up as you go". As long as you strive for continuous improvement, and trust and empower your people, you are setting yourself up for success.

Good examples of when to use the three phase advice process include utilising it for decisions, for example where or if the organisation should open a new office, or if an associate should attend a conference, or even renaming Slack channels to follow naming conventions. Not surprisingly, the latter ended up being a heated debate. The advice process is a tough but powerful process that can help organisations scale effectively.